Abstract
It aims to improve the degree of visualization of building data, ensure the ability of intelligent detection, and effectively solve the problems encountered in building data processing. Convolutional neural network and augmented reality technology are adopted, and a building visualization model based on convolutional neural network and augmented reality is proposed. The performance of the proposed algorithm is further confirmed by performance verification on public datasets. It is found that the building target detection model based on convolutional neural network and augmented reality has obvious advantages in algorithm complexity and recognition accuracy. It is 25 percent more accurate than the latest model. The model can make full use of mobile computing resources, avoid network delay and dependence, and guarantee the real-time requirement of data processing. Moreover, the model can also well realize the augmented reality navigation and interaction effect of buildings in outdoor scenes. To sum up, this study provides a research idea for the identification, data processing, and intelligent detection of urban buildings.
Highlights
With the rapid economic and social progression, the number of urban populations is increasing, and various urban management problems have become prominent
As the most extensive and common artificial features in urban areas, buildings play an important role in the construction of digital cities [3]. e most common building image detection is based on remote sensing image recognition
Compared with the model FPGA in latest research, the performance of the proposed one is increased by 25%. e proposed model generates the smallest proportion in the size of the file generation, only 17.8 Mb, which is about 5 times the speed of the original single shot MultiBox detector (SSD) model
Summary
With the rapid economic and social progression, the number of urban populations is increasing, and various urban management problems have become prominent. E focus of digital city construction is the collection and processing of data in urban facilities, enterprises, shops, and buildings. As the most extensive and common artificial features in urban areas, buildings play an important role in the construction of digital cities [3]. The recognition algorithm for building images is unable to extract effective information from the images due to technical limitations, which leads to incomplete data collected for digital city construction and delays in urban governance and layout decisions [5]. As many images cannot effectively correspond to the real city construction, their data processing and detection ability are weak, which further hinders the progression of digital city construction [6]. As many images cannot effectively correspond to the real city construction, their data processing and detection ability are weak, which further hinders the progression of digital city construction [6]. erefore, to ensure the degree of building data visualization, improve the ability of intelligent detection, and effectively solve the problems encountered in building data processing, it is necessary to construct and optimize the existing process of building data processing and intelligent detection
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